Overview

Dataset statistics

Number of variables17
Number of observations9471
Missing cells37353
Missing cells (%)23.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.2 MiB
Average record size in memory136.0 B

Variable types

Categorical2
Numeric13
Unsupported2

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Date has a high cardinality: 391 distinct valuesHigh cardinality
CO(GT) is highly overall correlated with PT08.S1(CO) and 8 other fieldsHigh correlation
PT08.S1(CO) is highly overall correlated with CO(GT) and 8 other fieldsHigh correlation
NMHC(GT) is highly overall correlated with CO(GT) and 9 other fieldsHigh correlation
C6H6(GT) is highly overall correlated with CO(GT) and 8 other fieldsHigh correlation
PT08.S2(NMHC) is highly overall correlated with CO(GT) and 8 other fieldsHigh correlation
NOx(GT) is highly overall correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S3(NOx) is highly overall correlated with CO(GT) and 8 other fieldsHigh correlation
NO2(GT) is highly overall correlated with CO(GT) and 7 other fieldsHigh correlation
PT08.S4(NO2) is highly overall correlated with CO(GT) and 8 other fieldsHigh correlation
PT08.S5(O3) is highly overall correlated with CO(GT) and 8 other fieldsHigh correlation
T is highly overall correlated with NMHC(GT) and 3 other fieldsHigh correlation
RH is highly overall correlated with THigh correlation
AH is highly overall correlated with PT08.S4(NO2) and 1 other fieldsHigh correlation
Date has 114 (1.2%) missing valuesMissing
Time has 114 (1.2%) missing valuesMissing
CO(GT) has 1797 (19.0%) missing valuesMissing
PT08.S1(CO) has 480 (5.1%) missing valuesMissing
NMHC(GT) has 8557 (90.3%) missing valuesMissing
C6H6(GT) has 480 (5.1%) missing valuesMissing
PT08.S2(NMHC) has 480 (5.1%) missing valuesMissing
NOx(GT) has 1753 (18.5%) missing valuesMissing
PT08.S3(NOx) has 480 (5.1%) missing valuesMissing
NO2(GT) has 1756 (18.5%) missing valuesMissing
PT08.S4(NO2) has 480 (5.1%) missing valuesMissing
PT08.S5(O3) has 480 (5.1%) missing valuesMissing
T has 480 (5.1%) missing valuesMissing
RH has 480 (5.1%) missing valuesMissing
AH has 480 (5.1%) missing valuesMissing
Unnamed: 15 has 9471 (100.0%) missing valuesMissing
Unnamed: 16 has 9471 (100.0%) missing valuesMissing
Date is uniformly distributedUniform
Time is uniformly distributedUniform
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-04-03 08:25:22.286209
Analysis finished2023-04-03 08:25:50.127300
Duration27.84 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct391
Distinct (%)4.2%
Missing114
Missing (%)1.2%
Memory size74.1 KiB
10/11/2004
 
24
02/12/2004
 
24
01/12/2004
 
24
30/11/2004
 
24
29/11/2004
 
24
Other values (386)
9237 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters93570
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10/03/2004
2nd row10/03/2004
3rd row10/03/2004
4th row10/03/2004
5th row10/03/2004

Common Values

ValueCountFrequency (%)
10/11/2004 24
 
0.3%
02/12/2004 24
 
0.3%
01/12/2004 24
 
0.3%
30/11/2004 24
 
0.3%
29/11/2004 24
 
0.3%
28/11/2004 24
 
0.3%
27/11/2004 24
 
0.3%
26/11/2004 24
 
0.3%
25/11/2004 24
 
0.3%
24/11/2004 24
 
0.3%
Other values (381) 9117
96.3%
(Missing) 114
 
1.2%

Length

2023-04-03T10:25:50.226182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10/11/2004 24
 
0.3%
20/09/2004 24
 
0.3%
12/03/2004 24
 
0.3%
13/03/2004 24
 
0.3%
14/03/2004 24
 
0.3%
15/03/2004 24
 
0.3%
16/03/2004 24
 
0.3%
17/03/2004 24
 
0.3%
18/03/2004 24
 
0.3%
19/03/2004 24
 
0.3%
Other values (381) 9117
97.4%

Most occurring characters

ValueCountFrequency (%)
0 30180
32.3%
/ 18714
20.0%
2 14805
15.8%
4 8844
 
9.5%
1 7902
 
8.4%
5 3903
 
4.2%
3 2670
 
2.9%
8 1656
 
1.8%
7 1656
 
1.8%
6 1632
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74856
80.0%
Other Punctuation 18714
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30180
40.3%
2 14805
19.8%
4 8844
 
11.8%
1 7902
 
10.6%
5 3903
 
5.2%
3 2670
 
3.6%
8 1656
 
2.2%
7 1656
 
2.2%
6 1632
 
2.2%
9 1608
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 18714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30180
32.3%
/ 18714
20.0%
2 14805
15.8%
4 8844
 
9.5%
1 7902
 
8.4%
5 3903
 
4.2%
3 2670
 
2.9%
8 1656
 
1.8%
7 1656
 
1.8%
6 1632
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30180
32.3%
/ 18714
20.0%
2 14805
15.8%
4 8844
 
9.5%
1 7902
 
8.4%
5 3903
 
4.2%
3 2670
 
2.9%
8 1656
 
1.8%
7 1656
 
1.8%
6 1632
 
1.7%

Time
Categorical

MISSING  UNIFORM 

Distinct24
Distinct (%)0.3%
Missing114
Missing (%)1.2%
Memory size74.1 KiB
18.00.00
 
390
05.00.00
 
390
14.00.00
 
390
13.00.00
 
390
12.00.00
 
390
Other values (19)
7407 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74856
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.00.00
2nd row19.00.00
3rd row20.00.00
4th row21.00.00
5th row22.00.00

Common Values

ValueCountFrequency (%)
18.00.00 390
 
4.1%
05.00.00 390
 
4.1%
14.00.00 390
 
4.1%
13.00.00 390
 
4.1%
12.00.00 390
 
4.1%
11.00.00 390
 
4.1%
10.00.00 390
 
4.1%
09.00.00 390
 
4.1%
08.00.00 390
 
4.1%
07.00.00 390
 
4.1%
Other values (14) 5457
57.6%

Length

2023-04-03T10:25:50.375233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18.00.00 390
 
4.2%
06.00.00 390
 
4.2%
20.00.00 390
 
4.2%
21.00.00 390
 
4.2%
22.00.00 390
 
4.2%
23.00.00 390
 
4.2%
00.00.00 390
 
4.2%
01.00.00 390
 
4.2%
02.00.00 390
 
4.2%
03.00.00 390
 
4.2%
Other values (14) 5457
58.3%

Most occurring characters

ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56142
75.0%
Other Punctuation 18714
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42498
75.7%
1 5067
 
9.0%
2 2730
 
4.9%
3 1170
 
2.1%
8 780
 
1.4%
4 780
 
1.4%
9 780
 
1.4%
5 779
 
1.4%
7 779
 
1.4%
6 779
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 18714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42498
56.8%
. 18714
25.0%
1 5067
 
6.8%
2 2730
 
3.6%
3 1170
 
1.6%
8 780
 
1.0%
4 780
 
1.0%
9 780
 
1.0%
5 779
 
1.0%
7 779
 
1.0%

CO(GT)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)1.3%
Missing1797
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2.1527495
Minimum0.1
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:50.497381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.1
median1.8
Q32.9
95-th percentile4.9
Maximum11.9
Range11.8
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.453252
Coefficient of variation (CV)0.67506786
Kurtosis2.6677794
Mean2.1527495
Median Absolute Deviation (MAD)0.8
Skewness1.3697528
Sum16520.2
Variance2.1119415
MonotonicityNot monotonic
2023-04-03T10:25:50.623477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 305
 
3.2%
1.4 279
 
2.9%
1.6 275
 
2.9%
1.5 273
 
2.9%
1.1 262
 
2.8%
0.7 260
 
2.7%
1.7 258
 
2.7%
1.3 253
 
2.7%
0.8 251
 
2.7%
0.9 248
 
2.6%
Other values (86) 5010
52.9%
(Missing) 1797
 
19.0%
ValueCountFrequency (%)
0.1 33
 
0.3%
0.2 45
 
0.5%
0.3 98
 
1.0%
0.4 160
1.7%
0.5 217
2.3%
0.6 244
2.6%
0.7 260
2.7%
0.8 251
2.7%
0.9 248
2.6%
1 305
3.2%
ValueCountFrequency (%)
11.9 1
< 0.1%
11.5 1
< 0.1%
10.2 2
< 0.1%
10.1 1
< 0.1%
9.9 1
< 0.1%
9.5 1
< 0.1%
9.4 1
< 0.1%
9.3 1
< 0.1%
9.2 1
< 0.1%
9.1 2
< 0.1%

PT08.S1(CO)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1041
Distinct (%)11.6%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1099.8332
Minimum647
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:50.760639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile810.5
Q1937
median1063
Q31231
95-th percentile1508
Maximum2040
Range1393
Interquartile range (IQR)294

Descriptive statistics

Standard deviation217.08004
Coefficient of variation (CV)0.19737542
Kurtosis0.33512865
Mean1099.8332
Median Absolute Deviation (MAD)142
Skewness0.75590737
Sum9888600
Variance47123.743
MonotonicityNot monotonic
2023-04-03T10:25:50.883855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
973 30
 
0.3%
1100 28
 
0.3%
988 26
 
0.3%
969 26
 
0.3%
925 26
 
0.3%
938 26
 
0.3%
966 25
 
0.3%
970 25
 
0.3%
984 25
 
0.3%
987 25
 
0.3%
Other values (1031) 8729
92.2%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
647 1
 
< 0.1%
649 1
 
< 0.1%
655 1
 
< 0.1%
667 3
< 0.1%
669 1
 
< 0.1%
676 1
 
< 0.1%
678 1
 
< 0.1%
679 1
 
< 0.1%
681 1
 
< 0.1%
683 2
< 0.1%
ValueCountFrequency (%)
2040 1
< 0.1%
2008 1
< 0.1%
1982 1
< 0.1%
1975 1
< 0.1%
1973 1
< 0.1%
1961 1
< 0.1%
1956 1
< 0.1%
1934 1
< 0.1%
1918 1
< 0.1%
1917 1
< 0.1%

NMHC(GT)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct429
Distinct (%)46.9%
Missing8557
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean218.81182
Minimum7
Maximum1189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:51.022664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile28.65
Q167
median150
Q3297
95-th percentile661.4
Maximum1189
Range1182
Interquartile range (IQR)230

Descriptive statistics

Standard deviation204.45992
Coefficient of variation (CV)0.93440987
Kurtosis2.270289
Mean218.81182
Median Absolute Deviation (MAD)94
Skewness1.5570171
Sum199994
Variance41803.859
MonotonicityNot monotonic
2023-04-03T10:25:51.171571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 14
 
0.1%
40 9
 
0.1%
29 9
 
0.1%
88 8
 
0.1%
93 8
 
0.1%
84 7
 
0.1%
55 7
 
0.1%
95 7
 
0.1%
60 7
 
0.1%
57 7
 
0.1%
Other values (419) 831
 
8.8%
(Missing) 8557
90.3%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
14 2
< 0.1%
16 1
 
< 0.1%
17 4
< 0.1%
18 2
< 0.1%
19 2
< 0.1%
ValueCountFrequency (%)
1189 1
< 0.1%
1129 1
< 0.1%
1084 1
< 0.1%
1042 1
< 0.1%
974 1
< 0.1%
926 1
< 0.1%
899 1
< 0.1%
880 1
< 0.1%
872 1
< 0.1%
840 1
< 0.1%

C6H6(GT)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct407
Distinct (%)4.5%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean10.083105
Minimum0.1
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:51.386636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.7
Q14.4
median8.2
Q314
95-th percentile24.65
Maximum63.7
Range63.6
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.4498197
Coefficient of variation (CV)0.7388418
Kurtosis2.4887059
Mean10.083105
Median Absolute Deviation (MAD)4.4
Skewness1.3615323
Sum90657.2
Variance55.499814
MonotonicityNot monotonic
2023-04-03T10:25:51.555052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 84
 
0.9%
2.8 82
 
0.9%
3.8 79
 
0.8%
4 78
 
0.8%
3.1 77
 
0.8%
3 76
 
0.8%
2.5 75
 
0.8%
2.9 73
 
0.8%
5.4 72
 
0.8%
2.2 71
 
0.7%
Other values (397) 8224
86.8%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.2 8
 
0.1%
0.3 10
 
0.1%
0.4 14
0.1%
0.5 20
0.2%
0.6 23
0.2%
0.7 31
0.3%
0.8 25
0.3%
0.9 25
0.3%
1 30
0.3%
ValueCountFrequency (%)
63.7 1
< 0.1%
52.1 1
< 0.1%
50.8 1
< 0.1%
50.7 1
< 0.1%
50.6 1
< 0.1%
49.5 1
< 0.1%
49.4 1
< 0.1%
48.2 1
< 0.1%
47.7 1
< 0.1%
47.5 1
< 0.1%

PT08.S2(NMHC)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1245
Distinct (%)13.8%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean939.15338
Minimum383
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:51.718402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum383
5-th percentile562
Q1734.5
median909
Q31116
95-th percentile1420
Maximum2214
Range1831
Interquartile range (IQR)381.5

Descriptive statistics

Standard deviation266.83143
Coefficient of variation (CV)0.28411912
Kurtosis0.063243873
Mean939.15338
Median Absolute Deviation (MAD)188
Skewness0.56156598
Sum8443928
Variance71199.011
MonotonicityNot monotonic
2023-04-03T10:25:51.857181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
853 25
 
0.3%
880 23
 
0.2%
800 23
 
0.2%
859 23
 
0.2%
985 22
 
0.2%
783 21
 
0.2%
769 21
 
0.2%
776 21
 
0.2%
850 21
 
0.2%
1012 20
 
0.2%
Other values (1235) 8771
92.6%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
383 2
< 0.1%
387 1
< 0.1%
388 1
< 0.1%
390 2
< 0.1%
397 1
< 0.1%
399 1
< 0.1%
402 2
< 0.1%
407 2
< 0.1%
408 1
< 0.1%
409 1
< 0.1%
ValueCountFrequency (%)
2214 1
< 0.1%
2007 1
< 0.1%
1983 1
< 0.1%
1981 1
< 0.1%
1980 1
< 0.1%
1959 1
< 0.1%
1958 1
< 0.1%
1935 1
< 0.1%
1924 1
< 0.1%
1920 1
< 0.1%

NOx(GT)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct925
Distinct (%)12.0%
Missing1753
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean246.89673
Minimum2
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:52.000782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile38
Q198
median180
Q3326
95-th percentile693
Maximum1479
Range1477
Interquartile range (IQR)228

Descriptive statistics

Standard deviation212.97917
Coefficient of variation (CV)0.86262448
Kurtosis3.4021344
Mean246.89673
Median Absolute Deviation (MAD)100
Skewness1.7157808
Sum1905549
Variance45360.126
MonotonicityNot monotonic
2023-04-03T10:25:52.129146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 41
 
0.4%
65 37
 
0.4%
93 36
 
0.4%
122 36
 
0.4%
41 36
 
0.4%
132 35
 
0.4%
95 35
 
0.4%
180 35
 
0.4%
51 34
 
0.4%
120 34
 
0.4%
Other values (915) 7359
77.7%
(Missing) 1753
 
18.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 3
< 0.1%
11 4
< 0.1%
12 4
< 0.1%
13 4
< 0.1%
ValueCountFrequency (%)
1479 1
< 0.1%
1389 2
< 0.1%
1369 1
< 0.1%
1358 1
< 0.1%
1345 1
< 0.1%
1310 1
< 0.1%
1301 1
< 0.1%
1290 1
< 0.1%
1253 1
< 0.1%
1247 1
< 0.1%

PT08.S3(NOx)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1221
Distinct (%)13.6%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean835.4936
Minimum322
Maximum2683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:52.507534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile483
Q1658
median806
Q3969.5
95-th percentile1291
Maximum2683
Range2361
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation256.81732
Coefficient of variation (CV)0.30738394
Kurtosis2.6775589
Mean835.4936
Median Absolute Deviation (MAD)155
Skewness1.1017292
Sum7511923
Variance65955.136
MonotonicityNot monotonic
2023-04-03T10:25:52.638045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
767 25
 
0.3%
733 25
 
0.3%
846 25
 
0.3%
765 23
 
0.2%
876 23
 
0.2%
685 22
 
0.2%
816 22
 
0.2%
891 22
 
0.2%
830 22
 
0.2%
845 22
 
0.2%
Other values (1211) 8760
92.5%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
322 1
< 0.1%
325 2
< 0.1%
328 1
< 0.1%
330 2
< 0.1%
334 1
< 0.1%
335 1
< 0.1%
340 2
< 0.1%
341 1
< 0.1%
345 1
< 0.1%
346 1
< 0.1%
ValueCountFrequency (%)
2683 1
< 0.1%
2559 1
< 0.1%
2542 1
< 0.1%
2331 1
< 0.1%
2327 1
< 0.1%
2318 1
< 0.1%
2294 1
< 0.1%
2121 1
< 0.1%
2095 2
< 0.1%
2081 1
< 0.1%

NO2(GT)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct283
Distinct (%)3.7%
Missing1756
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean113.09125
Minimum2
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:52.785581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile43
Q178
median109
Q3142
95-th percentile200.3
Maximum340
Range338
Interquartile range (IQR)64

Descriptive statistics

Standard deviation48.370108
Coefficient of variation (CV)0.42770866
Kurtosis0.46503212
Mean113.09125
Median Absolute Deviation (MAD)32
Skewness0.62171431
Sum872499
Variance2339.6673
MonotonicityNot monotonic
2023-04-03T10:25:52.906608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 78
 
0.8%
117 77
 
0.8%
119 77
 
0.8%
95 75
 
0.8%
101 75
 
0.8%
114 75
 
0.8%
110 74
 
0.8%
115 73
 
0.8%
116 72
 
0.8%
107 72
 
0.8%
Other values (273) 6967
73.6%
(Missing) 1756
 
18.5%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
13 1
 
< 0.1%
14 5
0.1%
ValueCountFrequency (%)
340 1
< 0.1%
333 1
< 0.1%
326 1
< 0.1%
322 1
< 0.1%
312 1
< 0.1%
310 1
< 0.1%
309 1
< 0.1%
306 1
< 0.1%
301 1
< 0.1%
296 1
< 0.1%

PT08.S4(NO2)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1603
Distinct (%)17.8%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1456.2646
Minimum551
Maximum2775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:53.034765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile883
Q11227
median1463
Q31674
95-th percentile2029
Maximum2775
Range2224
Interquartile range (IQR)447

Descriptive statistics

Standard deviation346.20679
Coefficient of variation (CV)0.23773619
Kurtosis0.078018624
Mean1456.2646
Median Absolute Deviation (MAD)221
Skewness0.20538853
Sum13093275
Variance119859.14
MonotonicityNot monotonic
2023-04-03T10:25:53.166845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1488 24
 
0.3%
1580 22
 
0.2%
1539 21
 
0.2%
1467 20
 
0.2%
1638 19
 
0.2%
1490 18
 
0.2%
1418 18
 
0.2%
1321 17
 
0.2%
1511 17
 
0.2%
1473 17
 
0.2%
Other values (1593) 8798
92.9%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
551 1
< 0.1%
559 1
< 0.1%
561 1
< 0.1%
579 1
< 0.1%
601 1
< 0.1%
602 1
< 0.1%
605 1
< 0.1%
621 1
< 0.1%
637 1
< 0.1%
640 1
< 0.1%
ValueCountFrequency (%)
2775 1
< 0.1%
2746 1
< 0.1%
2691 1
< 0.1%
2684 1
< 0.1%
2679 1
< 0.1%
2667 1
< 0.1%
2665 1
< 0.1%
2662 1
< 0.1%
2643 2
< 0.1%
2641 2
< 0.1%

PT08.S5(O3)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1743
Distinct (%)19.4%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1022.9061
Minimum221
Maximum2523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:53.297122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile461
Q1731.5
median963
Q31273.5
95-th percentile1761.5
Maximum2523
Range2302
Interquartile range (IQR)542

Descriptive statistics

Standard deviation398.48429
Coefficient of variation (CV)0.38956095
Kurtosis0.078612339
Mean1022.9061
Median Absolute Deviation (MAD)261
Skewness0.6278645
Sum9196949
Variance158789.73
MonotonicityNot monotonic
2023-04-03T10:25:53.429596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
836 20
 
0.2%
825 20
 
0.2%
826 19
 
0.2%
926 18
 
0.2%
799 17
 
0.2%
777 17
 
0.2%
949 16
 
0.2%
891 16
 
0.2%
923 16
 
0.2%
905 16
 
0.2%
Other values (1733) 8816
93.1%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
221 1
< 0.1%
225 1
< 0.1%
227 1
< 0.1%
232 1
< 0.1%
252 1
< 0.1%
253 1
< 0.1%
257 1
< 0.1%
261 2
< 0.1%
262 1
< 0.1%
263 1
< 0.1%
ValueCountFrequency (%)
2523 1
< 0.1%
2522 1
< 0.1%
2519 1
< 0.1%
2515 1
< 0.1%
2494 1
< 0.1%
2480 1
< 0.1%
2475 1
< 0.1%
2465 1
< 0.1%
2452 1
< 0.1%
2434 1
< 0.1%

T
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct436
Distinct (%)4.8%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean18.317829
Minimum-1.9
Maximum44.6
Zeros1
Zeros (%)< 0.1%
Negative13
Negative (%)0.1%
Memory size74.1 KiB
2023-04-03T10:25:53.561664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.9
5-th percentile4.6
Q111.8
median17.8
Q324.4
95-th percentile34.5
Maximum44.6
Range46.5
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation8.8321157
Coefficient of variation (CV)0.48215953
Kurtosis-0.45627382
Mean18.317829
Median Absolute Deviation (MAD)6.3
Skewness0.30935679
Sum164695.6
Variance78.006268
MonotonicityNot monotonic
2023-04-03T10:25:53.677395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.8 57
 
0.6%
21.3 54
 
0.6%
13.8 51
 
0.5%
20.2 51
 
0.5%
12.3 49
 
0.5%
15.6 49
 
0.5%
12 49
 
0.5%
16.3 48
 
0.5%
19.8 48
 
0.5%
14.6 47
 
0.5%
Other values (426) 8488
89.6%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
-1.9 1
< 0.1%
-1.4 1
< 0.1%
-1.3 2
< 0.1%
-1.2 1
< 0.1%
-1.1 1
< 0.1%
-0.6 2
< 0.1%
-0.5 1
< 0.1%
-0.3 1
< 0.1%
-0.2 1
< 0.1%
-0.1 2
< 0.1%
ValueCountFrequency (%)
44.6 1
 
< 0.1%
44.3 1
 
< 0.1%
43.4 1
 
< 0.1%
43.1 1
 
< 0.1%
42.8 3
< 0.1%
42.7 1
 
< 0.1%
42.6 1
 
< 0.1%
42.5 1
 
< 0.1%
42.2 2
< 0.1%
42 2
< 0.1%

RH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct753
Distinct (%)8.4%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean49.234201
Minimum9.2
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:53.811112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile20.3
Q135.8
median49.6
Q362.5
95-th percentile77.9
Maximum88.7
Range79.5
Interquartile range (IQR)26.7

Descriptive statistics

Standard deviation17.316892
Coefficient of variation (CV)0.35172486
Kurtosis-0.81837452
Mean49.234201
Median Absolute Deviation (MAD)13.3
Skewness-0.03792801
Sum442664.7
Variance299.87476
MonotonicityNot monotonic
2023-04-03T10:25:53.943029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.1 31
 
0.3%
47.8 30
 
0.3%
57.9 30
 
0.3%
60.8 27
 
0.3%
45.9 27
 
0.3%
43.4 26
 
0.3%
50.9 26
 
0.3%
49.8 26
 
0.3%
50.1 26
 
0.3%
50.8 26
 
0.3%
Other values (743) 8716
92.0%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
9.2 2
< 0.1%
9.3 1
< 0.1%
9.6 1
< 0.1%
9.8 1
< 0.1%
9.9 2
< 0.1%
10 2
< 0.1%
10.2 1
< 0.1%
10.4 1
< 0.1%
10.7 1
< 0.1%
10.9 1
< 0.1%
ValueCountFrequency (%)
88.7 1
 
< 0.1%
87.2 1
 
< 0.1%
87.1 1
 
< 0.1%
87 1
 
< 0.1%
86.6 2
< 0.1%
86.5 2
< 0.1%
86 1
 
< 0.1%
85.7 3
< 0.1%
85.6 1
 
< 0.1%
85.5 1
 
< 0.1%

AH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6683
Distinct (%)74.3%
Missing480
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1.0255303
Minimum0.1847
Maximum2.231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.1 KiB
2023-04-03T10:25:54.094850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1847
5-th percentile0.40085
Q10.7368
median0.9954
Q31.3137
95-th percentile1.7256
Maximum2.231
Range2.0463
Interquartile range (IQR)0.5769

Descriptive statistics

Standard deviation0.40381261
Coefficient of variation (CV)0.39375981
Kurtosis-0.56009784
Mean1.0255303
Median Absolute Deviation (MAD)0.2861
Skewness0.25138776
Sum9220.5427
Variance0.16306462
MonotonicityNot monotonic
2023-04-03T10:25:54.210994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7487 6
 
0.1%
0.8394 6
 
0.1%
0.9684 6
 
0.1%
0.9722 6
 
0.1%
1.1199 6
 
0.1%
0.8325 5
 
0.1%
0.8736 5
 
0.1%
0.6686 5
 
0.1%
1.0594 5
 
0.1%
0.9271 5
 
0.1%
Other values (6673) 8936
94.4%
(Missing) 480
 
5.1%
ValueCountFrequency (%)
0.1847 1
< 0.1%
0.1862 1
< 0.1%
0.191 1
< 0.1%
0.1975 1
< 0.1%
0.1988 1
< 0.1%
0.2029 1
< 0.1%
0.2031 1
< 0.1%
0.2062 1
< 0.1%
0.2086 1
< 0.1%
0.2157 1
< 0.1%
ValueCountFrequency (%)
2.231 1
< 0.1%
2.1806 1
< 0.1%
2.1766 1
< 0.1%
2.1719 1
< 0.1%
2.1395 1
< 0.1%
2.1362 1
< 0.1%
2.1247 1
< 0.1%
2.1195 1
< 0.1%
2.117 1
< 0.1%
2.1164 1
< 0.1%

Unnamed: 15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9471
Missing (%)100.0%
Memory size74.1 KiB

Unnamed: 16
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9471
Missing (%)100.0%
Memory size74.1 KiB

Interactions

2023-04-03T10:25:47.478166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:24.218054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.953247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.505152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:29.046218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.393060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.454384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:36.690889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.533395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.456016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.430429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.178935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.763767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:47.831894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:24.365677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.086288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.618141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:29.217234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.544168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.591682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:36.917341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.667581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.575822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.548576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.307289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.889672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:47.962107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:24.529074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.217707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.731713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:29.435323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.676651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.739552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.092272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.813999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.710811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.679200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.441878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.021829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.095562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:24.669920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.343691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.851357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:29.690720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.850868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.880756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.244022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.944809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.873410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.841088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.567600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.143018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.222394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:24.813438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.476045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.973702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:29.978208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:33.044487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:35.072934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.364495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:39.074412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.029220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.973722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.691610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.266222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.346441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:24.965796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.606729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.082549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:30.271846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:33.216977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:35.229517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.505830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:39.210540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.206420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.111227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.827869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.391993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.470380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.112254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.729388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.245247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:30.547501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:33.378281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:35.447463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.654724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:39.347557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.362268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.265802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.957392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.521458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.581694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.249461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.839610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.367480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:31.250267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:33.548512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:35.648046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.774316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:39.463027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.505930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.376294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.079964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.643802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.704672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.389289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:26.948262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.483622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:31.515655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:33.727679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:35.884513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:37.892322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:39.598040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.662963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.502851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.204421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.776205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.812423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.517782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.061269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.600372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:31.642969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:33.877381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:36.018768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.012094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:39.727743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.813187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.615321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.309622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:46.888539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:48.916438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.630916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.167287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.703581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.026827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.014395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:36.188093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.138625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.074986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:41.994711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.732583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.426768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:47.045793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:49.017376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.731725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.273906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.817793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.137274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.165329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:36.330802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.256228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.192849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.142237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:43.861329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.542340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:47.187156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:49.144284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:25.842237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:27.396339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:28.932451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:32.265724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:34.333002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:36.493481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:38.410691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:40.325402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:42.319981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:44.045315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:45.664733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T10:25:47.354099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-03T10:25:54.325827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
CO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHTime
CO(GT)1.0000.8790.9360.9270.9270.785-0.8110.7350.5940.8560.069-0.0060.0530.232
PT08.S1(CO)0.8791.0000.8510.8890.8890.725-0.8540.6730.6460.8940.0810.0920.1380.207
NMHC(GT)0.9360.8511.0000.9500.9500.884-0.9500.8420.8950.8040.506-0.2780.2930.244
C6H6(GT)0.9270.8890.9501.0001.0000.705-0.8500.6690.7490.8740.276-0.1220.1920.230
PT08.S2(NMHC)0.9270.8890.9501.0001.0000.705-0.8500.6690.7490.8740.276-0.1220.1920.255
NOx(GT)0.7850.7250.8840.7050.7051.000-0.7900.8350.1600.793-0.2830.182-0.1900.151
PT08.S3(NOx)-0.811-0.854-0.950-0.850-0.850-0.7901.000-0.689-0.536-0.862-0.118-0.084-0.2170.180
NO2(GT)0.7350.6730.8420.6690.6690.835-0.6891.0000.1540.709-0.190-0.121-0.3230.213
PT08.S4(NO2)0.5940.6460.8950.7490.7490.160-0.5360.1541.0000.5610.614-0.0660.6450.173
PT08.S5(O3)0.8560.8940.8040.8740.8740.793-0.8620.7090.5611.000-0.0020.1280.0830.170
T0.0690.0810.5060.2760.276-0.283-0.118-0.1900.614-0.0021.000-0.5430.7010.157
RH-0.0060.092-0.278-0.122-0.1220.182-0.084-0.121-0.0660.128-0.5431.0000.1560.196
AH0.0530.1380.2930.1920.192-0.190-0.217-0.3230.6450.0830.7010.1561.0000.000
Time0.2320.2070.2440.2300.2550.1510.1800.2130.1730.1700.1570.1960.0001.000

Missing values

2023-04-03T10:25:49.307403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-03T10:25:49.595361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-03T10:25:49.896265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHUnnamed: 15Unnamed: 16
010/03/200418.00.002.61360.0150.011.91046.0166.01056.0113.01692.01268.013.648.90.7578NaNNaN
110/03/200419.00.002.01292.0112.09.4955.0103.01174.092.01559.0972.013.347.70.7255NaNNaN
210/03/200420.00.002.21402.088.09.0939.0131.01140.0114.01555.01074.011.954.00.7502NaNNaN
310/03/200421.00.002.21376.080.09.2948.0172.01092.0122.01584.01203.011.060.00.7867NaNNaN
410/03/200422.00.001.61272.051.06.5836.0131.01205.0116.01490.01110.011.259.60.7888NaNNaN
510/03/200423.00.001.21197.038.04.7750.089.01337.096.01393.0949.011.259.20.7848NaNNaN
611/03/200400.00.001.21185.031.03.6690.062.01462.077.01333.0733.011.356.80.7603NaNNaN
711/03/200401.00.001.01136.031.03.3672.062.01453.076.01333.0730.010.760.00.7702NaNNaN
811/03/200402.00.000.91094.024.02.3609.045.01579.060.01276.0620.010.759.70.7648NaNNaN
911/03/200403.00.000.61010.019.01.7561.0NaN1705.0NaN1235.0501.010.360.20.7517NaNNaN
DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHUnnamed: 15Unnamed: 16
9461NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9462NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9463NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9464NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9465NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9466NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9467NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9468NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9469NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9470NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH# duplicates
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN114